1. Field
One or more example embodiments of the following description relate to multimedia processing. More particularly, one or more example embodiments of the following description relate to estimating position of a head of an individual in an image.
2. Description of the Related Art
Computationally deriving a position of the head of a subject (a person or an individual) is one of the greatest challenges in the domain of Human Computer Interaction (HCI). Movement of the head is confined to various types, which include pitch, roll and yaw. Human beings tend to make such head movements with varying frequency. One of the requirements in research areas such as HCI is to estimate head poses dynamically or statically. The application areas of HCI include customer feedback, biological pose correction, gaze interfaces and so on. Some of the inferences that can be derived from the head pose estimation results include intent estimation, emotion and facial expression recognition and the like.
Further, in order to determine the position of head, a series of computational operations need to be performed. Some of the known computational techniques may include a neural network approach, a probabilistic approach, 3-D model based tracking, machine learning techniques, etc. For instance, the 3D model based tracking system generates or constructs a model every time the subject appears in front of the tracking system or uses the tracking system. This operation of generating models is time consuming and cannot be implemented in applications such as surveillance in public places, shopping malls, etc. In another instance, an image is captured to infer and identify parts of the head (e.g. eyes, nose, hair, ears, cheek, chin, lips, and ears) in machine learning techniques. However, such inferring requires both high scale equipment and computational capability associated with determining of the head position.